Recently, methods with pseudo-tasks have been proposed for AD.
One major line of work is to predict geometric transformations [1]}, [2]}, such as rotation or random transformations, and use the prediction errors to detect anomalies.
[3]} instead learns a set of diverse transformations and show improvements for tabular and text data.
CutPaste [4]} learns to classify the images with patches replaced by another image, combined with density estimation in the latent space for image data.
